Camera-based boom control

ABSTRACT

Systems and methods are described for determining an actual pose of an articulating boom arm using an artificial intelligence mechanism (e.g., a neural network) trained to determine the actual pose of the articulating boom arm based on captured image data. In some implementations, an electronic processor is configured to control movement of the articulating boom arm based at least in part on pose information determined by applying the image-based neural network. In some implementations, an electronic processor is configured to train the neural network by using, as training data, captured image data and output signals from sensors indicative of measured positions of the components of the articulating boom arm.

BACKGROUND

The present invention relates to systems and methods for controlling theoperation of a boom arm (for example, a boom arm of a feller buncher orexcavator).

SUMMARY

In one embodiment, the invention provides a machine—for example, afeller buncher or an excavator—that includes a machine body and anarticulating boom arm. The articulating boom arm includes a hoist boomcoupled to the machine body and a stick boom coupled to a distal end ofthe hoist boom. A hoist actuator is configured to controllably adjust anangle of the hoist boom relative to the machine body and a stickactuator is configured to controllably adjust an angle of the stick boomrelative to the hoist boom. A hoist sensor outputs a signal indicativeof the angle of the hoist boom and a stick sensor outputs a signalindicative of the angle of the stick boom. The machine also includes acamera mounted to the machine with a field of view that includes atleast a part of the machine and the boom system. An electronic processoris configured to train a neural network to determine, based on imagedata captured by the camera, an actual angle of the hoist boom and/or anactual angle of the stick boom. The signal output by the stick sensor,the signal output by the hoist sensor, and image data captured by thecamera are used as training input to train the neural network. In someimplementations, the neural network is trained to determine the anglesof the hoist boom and the stick boom based solely an instant digitizedvideo image from the one or more cameras as the only input(s) to theneural network. The electronic processor is also configured to determinean actual pose of the articulating boom arm based on the signal outputfrom the hoist sensor, the signal output of the stick sensor, and/or theoutput of the neural network (based on image data captured by thecamera) and to operate the hoist actuator and the stick actuator basedat least in part on the determined actual pose of the articulating boomarm. In some embodiments, the controller is configured to use the hoistsensor and the stick sensor as the primary mechanism for determining theactual pose of the articulating boom arm and to use the output of theneural network to determine the actual pose of the articulating boom armonly when one or more of the sensors have failed or are missing.

In another embodiment, the invention provides a method for controllingmovement of an articulating boom arm of a machine. At least one image iscaptured by a camera mounted on the machine with a field of view thatincludes at least a portion of the machine. An artificial intelligencemechanism (e.g., processing captured image data through a trained neuralnetwork) is applied with the at least one captured image as an input.The artificial intelligence mechanism is trained to output at least onevalue indicative of a pose of the articulating boom arm based on the atleast one image from the camera as the input. An actuator configured tocause movement of the articulating boom arm is then operated based atleast in part on the output of the artificial intelligence mechanism.

In yet another embodiment, the invention provides a method of training aneural network for determining a pose of an articulating boom arm of amachine based on captured image data. A signal is received by anelectronic processor from a sensor. The signal is indicative of ameasured pose of at least one part of the articulating boom arm and thesensor is configured to directly measure the pose of the at least onepart of the articulating boom arm. The electronic processor alsoreceives an image of at least part of the machine captured by a cameramounted to the machine. The neural network is then trained to determinethe pose of the at least one part of the articulating boom arm based onone or more captured images. The signal indicative of the measured posedfrom the sensor and the image captured by the camera are used astraining input to train the neural network.

Other aspects of the invention will become apparent by consideration ofthe detailed description and accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an elevation view of a feller buncher according to oneembodiment.

FIG. 2 is a block diagram of a control system for the feller buncher ofFIG. 1.

FIG. 3 is a flowchart of a method for training a neural network fordetermining a pose of a boom arm of the feller buncher of FIG. 1 whilealso controlling movement of the boom arm.

FIG. 4 is a flowchart of a method for using a trained neural networkbased on camera images as a back-up mechanism for determining a pose ofthe boom arm of the feller buncher of FIG. 1.

FIG. 5 is a flowchart of a method for controlling a pose of the boom armof the feller buncher of FIG. 1 using a trained neural network based oncamera images.

DETAILED DESCRIPTION

Before any embodiments of the invention are explained in detail, it isto be understood that the invention is not limited in its application tothe details of construction and the arrangement of components set forthin the following description or illustrated in the following drawings.The invention is capable of other embodiments and of being practiced orof being carried out in various ways.

FIG. 1 illustrates an example of a heavy machinery (e.g., a fellerbuncher 100) equipped with an articulated boom arm. The feller buncher100 includes a main vehicle body 101 with an operator cab 103. Anoperator of the feller buncher 100 sits within the operator cab 103 andoperates the movement of the main vehicle body 101 and the articulatedboom arm while visually monitoring the equipment through one or morewindows of the operator cab 103. The articulated boom arm includes ahoist boom 105 pivotably coupled to the main vehicle body 101. A stickboom 107 is pivotably coupled to a distal end of the hoist boom 105 andan end effector 109 is coupled to a distal end of the stick boom 107.

A hoist cylinder 111 is coupled between the main vehicle body 101 andthe hoist boom 105 and configured to controllably raise and lower thehoist boom 105. Extending a piston of the hoist cylinder 111 increasesthe angle of the hoist boom 105 relative to the ground (i.e., “raising”the hoist boom 105) while retracting the piston of the hoist cylinder111 decreases the angle of the hoist boom 105 relative to the ground(i.e., “lowering” the hoist boom 105). Similarly, a stick cylinder 113is coupled between the hoist boom 105 and the stick boom 107. Extendinga piston of the stick cylinder 113 increases the angle of the stick boom107 relative to the hoist boom 105 while retracting the piston of thestick cylinder 113 decreases the angle of the stick boom 107 relative tothe hoist boom 105. Finally, a tilt cylinder 115 is coupled between thestick boom 107 and the end effector 109 and is configured tocontrollably adjust a tilt angle of the end effector 109 relative to thestick boom 107.

Although the example of FIG. 1 and the discussion below referspecifically to a feller buncher, the systems and methods describedherein can be readily applied to other machinery including, for example,an excavator.

FIG. 2 illustrates an example of a control system for the feller buncher100 of FIG. 1. The control system includes a controller 201 with anelectronic processor 203 and a non-transitory computer-readable memory205. The memory 205 stores instructions that are executed by theelectronic processor 203 to provide the control functionality of thecontroller 201 including, for example, the functions described herein.The controller 201 is communicatively coupled (e.g., through a wired orwireless connection) to a hoist cylinder actuator 207, a stick cylinderactuator 209, and a tilt cylinder actuator 211. The controller 201provides control signals/instructions to the cylinder actuators 207,209, 211 to control the extending and retracting of the piston of eachcylinder. In this way, the controller 201 can regulate and controllerthe raising, lowering, and tilting of the hoist boom 105, the stick boom107, and the end effector 109.

In some implementations, the hoist cylinder 111, the stick cylinder 113,and the tilt cylinder 115 are all hydraulic cylinders in which a pistonis extended by increasing the hydraulic pressure within the cylinder andthe piston is retracted by decreasing the hydraulic pressure within thecylinder. In some such implementations, the hoist cylinder actuator 207,the stick cylinder actuator 209, and the tilt cylinder actuator 211includes one or more hydraulic pumps and/or regulator valves. Forexample, in some implementations, each cylinder actuator 207, 209, 211includes a separate hydraulic pump that is controlled based on signalsfrom the controller 201 and configured to extend the cylinder piston bypumping hydraulic fluid into an individual hydraulic cylinder. In someimplementations, each cylinder actuator 207, 209, 211 includes apressure release valve that is controlled based on signals from thecontroller 201 and configured to retract the cylinder piston byreleasing hydraulic fluid from an individual hydraulic cylinder into afluid reservoir. In some implementations, the cylinder actuators 207,209, 211 includes a single shared hydraulic pump and a series ofcontrollable valves—the controllable valves operate based on signalsfrom the controller 201 and each individual cylinder piston is extendedby controllably opening the respective pressure input valve for thecylinder while the shared hydraulic pump operates causing hydraulicfluid to flow into the respectively cylinder through the open pressureinput valve.

To operate the articulation of the boom arm, the controller 201 receivesone or more input signals indicative of a movement or positioninginstruction. In some implementations, these signals are received fromone or more user operated controls—for example, a “joystick” control.Some implementations also provide intelligent boom control functions byproviding on-the-fly data regarding the current pose of the boom arm tothe controller 201. The controller 201 is then configured toadjust/control the pose of the boom arm based, at least in part, on thecurrent pose information and other control signals. For example, thecontroller 201 may be configured to determine an actual pose of the boomarm, determine a target pose of the boom arm, and control the cylinderactuators 207, 209, 211 to cause the actual pose of the boom arm toapproach the target pose.

To facilitate control of the boom arm pose (using intelligent boomcontrol functions or otherwise), it may be necessary for the controller201 to determine the relative angles of each portion of the boom arm.For example, the controller 201 may determine an angle of the hoist boom105 relative to the ground, an angle of the stick boom 107 relative tothe hoist boom 105, and a tilt angle of the end effector 109 relative tothe stick boom 107. In some implementations, these relative angles maybe determined using angle displacement sensors (e.g., a Hall effectsensor) incorporated into the rotation joint between each component ofthe boom arm. In other implementations, the cylinders 111, 113, 115 thatcontrollable adjust the relative angular positions of each component ofthe boom arm equipped with a displacement sensor that measures thelinear displacement of the piston of each hydraulic cylinder 111, 113,115. Based on this measured displacement of the cylinder piston(s), thecontroller 201 is able to determine an angular position of eachcomponent of the boom arm. For example, based on the measureddisplacement of the piston of the hoist cylinder 111, the controller 201can calculate the angular position of the hoist boom 105 relative to themain vehicle body 101; based on the measured displacement of the pistonof the stick cylinder 113, the controller 201 can calculate the angularposition of the stick boom 107 relative to the hoist boom 105; and,based on the measured displacement of the tilt cylinder 115, thecontroller 201 can calculate the tilt angle of the end effector 109relative to the stick boom 107.

In the example of FIG. 2, the controller 201 is also communicativelycoupled to a hoist displacement sensor 213, a stick displacement sensor215, and a tilt displacement sensor 217 and is configured to determinethe relative angular position of each component of the boom arm (i.e.,the hoist boom 105, the stick boom 107, and the end effector 109) basedon signals received from the displacement sensors 213, 215, 217indicative of the linear displacement of the piston of each respectivecylinder 111, 113, 115.

However, in some implementation, it may be advantageous to incorporateanother mechanism for determining a pose of the boom arm in addition toor instead of the series of displacement sensors 213, 215, 217.Accordingly, in the example of FIG. 2, the controller 201 is alsocommunicatively coupled to one or more cameras 219. Each camera 219 ispositioned on the machine 100 with a field of view that includes atleast part of the articulating boom arm. For example, a camera 219 maybe mounted at the end effector 109 with a field of view facing towardsthe operator cab 103. Additionally or alternatively, a camera 219 may bemounted on (or in) the operator cab with a field of view facing outwardtowards the end effector/boom arm.

In systems that include one or more cameras, the controller 201 may beconfigured to utilize an artificial intelligence mechanism (e.g., aneural network) to determine a pose of the articulating boom based onone or more images captured by the camera(a) 219. In someimplementations, one or more camera images are provided as input to theneural network and the output of the neural network indicates a positionof the articulating boom arm. For example, the controller 201 may beconfigured to implement a single neural network that receives one ormore images as input and provides the same three outputs as thedisplacement sensors (e.g, a displacement of the hoist cylinder 111, adisplacement of the stick cylinder 113, and a displacement of the tiltcylinder 115). In other implementations, the neural network may beconfigured to instead provide three angle values as outputs eachindicative of a relative angular position of a different component ofthe boom arm (e.g., an angle of the hoist boom 105 relative to the mainvehicle body 101, an angle of the stick boom 107 relative to the hoistboom 105, and a tilt angle of the end effector 109 relative to the stickboom 107).

In still other implementations, the neural network may be configured toprovide more, fewer, or different outputs. For example, a neural networkmay be configured to receive as input an image captured by the cameramounted to the end effector and to provide as outputs an indication ofthe displacement of the stick cylinder and the displacement of the hoistcylinder (i.e., without providing an indication of the tilt angle of theend effector). In other implementations, for example, the neural networkmay be configured to provide as output an indication of the position andtilt angle of the end effector 109 relative to the ground or relative tothe main vehicle body 101 without provide any outputs indicative of therelative positions of the stick boom 107 or the hoist boom 105.

Various different implementations are possible for equipping a machine100 with one or more cameras and a controller configured to determine apose of the boom arm based on the camera images. For example, themachine 100 may be equipped with both a set of displacement sensors 213,215, 217 and one or more cameras 219 (as illustrated in FIG. 2). In someimplementations, this type of configuration can be used exclusively totrain the neural network based on the captured camera images and thesensed displacement of the cylinders. In other implementations, thecamera based neural network can be used as a redundant sensing mechanismand/or as a fallback system in case of a failure of one or more of thecylinder displacement sensors. In some implementations where thecamera-based neural network is provided as a fallback sensing mechanism,the controller 201 can be configured to continually or periodicallytrain the neural network based on captured image data and the output ofthe displacement sensors while the displacement sensors are operatingproperly. In still other implementations, a machine 100 might beequipped only with the camera-based sensing system (and a previouslytrained neural network)—the displacement sensors 213, 215, 217 may beomitted entirely to reduce cost and/or complexity of the machine 100. Insome implementations, one or more of the displacement sensors 213, 215,217 may include an inertial monitoring unit (IMU).

FIG. 3 illustrates an example of a method for operating a boom arm basedon the outputs from one or more displacement sensors while training aneural network based on the displacement sensor outputs and capturedimages. The system determines a target pose/angle/displacement for thehoist boom 105 (step 301) and a target pose/angle/displacement for thestick boom 107 (step 303)—for example, based on a user control input oras determined by an intelligent boom control function. The system thenreceives outputs from the hoist displacement sensor 213 (step 305) andfrom the stick displacement sensor 215 (step 307). If the measured hoistdisplacement does not match the hoist target (step 309), then thecontroller 201 sends a signal to the hoist cylinder actuator 207 toadjust a position of the hoist boom 105 towards the hoist target (step311). Similarly, if the measured stick displacement does not match thestick target (step 313), the controller 201 sends a signal to the stickcylinder actuator 209 to adjust a position of the stick boom towards thetarget (step 315).

While operating the boom arm based on the measured outputs of thedisplacement sensors, the controller 201 also periodically capturesimages through the cameras mounted on the machine 100 (step 317) andprovides the captured image along with the corresponding measureddisplacements from the hoist displacement sensor 213 and the stickdisplacement sensor 215 as training inputs to train the neural network(step 319).

The method of FIG. 3 might be used, for example, by a manufacturer totrain an image-based neural network under test conditions so that thetrained neural network can then be installed on machines in the field orprior to the sale of field machines. However, as noted above, in otherimplementations, the controller 201 might instead be configured for usein a field machine to operate the boom arm based on displacement datafrom the displacement sensors while the displacement sensors areoperating properly and to operate the boom arm based on the output ofthe image-based neural network in the event of a failure of one or moredisplacement sensors.

FIG. 4 illustrates an example of a method in which the image-basedneural network is used as a back-up to the displacement sensors andwhere the neural network is further trained by the controller 201 whilethe displacement sensors are operating properly. The controller 201receives (or determines) one or more boom position inputs defining atarget position or movement of the boom arm (step 401). The controller201 also captures one or more images from the one or more cameras 219mounted on the machine 100 (step 403). The controller 201 thendetermines whether the hoist sensor is available (step 405). If so, theposition of the hoist boom 105 is determined based on the output of thehoist displacement sensor 213 (step 407). However, if the hoist sensoris not available (e.g., has been removed or damaged), the controller 201applies the neural network to determine the position of the hoist boom105 based on the captured image(s) (step 409). Similarly, the controller201 then determines whether the stick sensor is available (step 411). Ifso, the controller 201 determines the position of the stick boom 107based on the output of the stick displacement sensor 215 (step 413).However, if the stick sensor 215 is not available (e.g., has beenremoved or damaged), the controller 201 applies the neural network todetermine the position of the stick boom 107 based on the capturedimage(s) (step 415). Furthermore, if the controller 201 determines thatboth the stick displacement sensor 215 and the hoist displacement sensor213 are available (step 417), then the controller 201 uses capturedcamera image(s) and the outputs of the displacement sensors to furthertrain the neural network (step 419). In either case (whether the hoist &stick positions are determined based on the displacement sensor or theimage-based neural network), the controller 201 adjusts the hoistcylinder actuator 207 and/or the stick cylinder actuator 209 based onthe boom position input(s) and the determined hoist/stick positions (ifnecessary) (step 421).

In the example of FIG. 4, the image-based neural network is usedstrictly as a “back-up” mechanism to determine the position of the hoistand/or stick booms 105, 107 in the event of a failure of one or more ofthe hoist displacement sensor 213 and the stick displacement sensor 215.However, in some implementations, the controller 201 is configured toutilize both the output of the displacement sensors and the output ofthe image-based neural network in controlling the movement/position ofthe boom arm. For example, in some implementations, the output of theimage-based neural network can be used to detect a failure of a cylinderdisplacement sensor. For example, the controller 201 might be configured(a) to calculate a difference between the output of the displacementsensor and the output of the image-based neural network and (b) todetermine that the displacement sensor has failed when the differenceexceeds a threshold or based on a rate of change of the calculateddifference.

Finally, as discussed above, the systems and methods described hereinmay be adapted to train the neural network under normal operatingconditions of a field machine or under test conditions. In someimplementations where the image-based neural network has already beentrained, a controller 201 may be configured to use the trained neuralnetwork as the primary mechanism for determining the pose of the boomarm. In some such implementations, the machine 100 might be configuredto not include any sensors for directly measuring the pose of the boomarm (e.g., displacement sensors or rotation/angle sensors).

FIG. 5 illustrates an example of a method for controlling the boom armof the machine 100 based only on the captured images and a previouslytrained image-based neural network. Again, the controller 201 receivesor determines one or more boom position input(s) (step 501) and capturesan image of the boom arm (step 503). However, in this example, thecontroller 201 applies the trained neural network to determine thepositions of the hoist boom 105 and the stick boom 107 based only on thecaptured image data (step 505). The controller 201 then adjusts thehoist cylinder actuator 207 and/or the stick cylinder actuator 209 asnecessary based on the determined positions and the boom positioninput(s) (step 507).

Although the examples illustrated in FIGS. 3, 4, and 5 only describemechanisms for determining the positions of the hoist boom 105 and thestick boom 107, these methods can be adapted to determine otherpositioning variables—for example, a tilt angle of the end effector—inaddition to or instead of the hoist boom 105 and/or the stick boom 107.

Additionally, although the examples discussed above primarily focus onone or more cameras positioned on the machine with at least a part ofthe boom arm in the field of view, in some implementations, the neuralnetwork might be trained to determine one or more position values of theboom arm based on images of other parts of the machine 100. For example,a camera might be positioned on the end effector 109 with at least apart of the operator cab 103 and/or the main vehicle body 101 positionedin its field of view and the neural network might be trained todetermine a position and/or tilt angle of the end effector relative tothe operator cab 103 based on changes in perspective of the operator cab103 in the images captured by the camera mounted to the end effector109.

Furthermore, the number of cameras used to train the neural networkand/or to operate the machine using the neural network can vary indifferent implementations. For example, in some implementations, thesystem may be configured to include only one camera positioned tocapture an image of the enter boom arm and to use images from thissingle camera to train the neural network or to determine the pose ofthe boom arm. In other implementations, the system may be configured toinclude multiple cameras each focused on a different part of themachine. For example, a first camera might be positioned on the body ofthe vehicle to capture an image of the hoist boom while a second camerais coupled to the hoist boom and configured to capture an image of thestick boom. In still other implementations, the system may be configuredto include multiple cameras positioned to capture multiple images of thesame machine components from various different angles. For example, afirst camera might be positioned on the vehicle body to capture imagesof all or part of the boom arm while a second camera is positioned onthe boom arm near the end effector to capture images of all or part ofthe boom arm and, in some cases, the machine body. As another example,the system might include multiple cameras mounted on the vehicle body ofthe machine both positioned to capture images of the boom arm fromdifferent perspectives. In some implementations, training and using theneural network with images from multiple different cameras can increasethe probability of a correct position determination.

In some implementations, the positioning and field of view of thecameras can be designed to limit the collection of image shapes to beprocessed. For example, a camera might be positioned with a positionthat is fixed relative to the boom arm and configured to capture animage of the vehicle body or a specific target component on the vehiclebody. Accordingly, image data captured by the camera would only need tobe processed to identify a more limited set of possibleshapes/orientations in order to determine a relative position of theboom arm.

Similarly, in some implementations, a special paint (e.g., a particularcolor paint, a reflective paid, or an infrared reflective paint) mightbe used on the boom arm and/or the vehicle body of the machine in orderto improve/simply image processing in distinguishing between parts ofthe machine and the image background in images captured by thecamera(s). In some implementations, distinctive colors or other markingscan be used on different components of the machine to simplify theprocessing required to distinguish individual components of the machinefrom the background and from other components of the machine. In someimplementations, numerical/digital filtered can be applied to thecaptured image before it is processed through the neural network (e.g.,for training of the neural network or for use of the neural network indetermining the position of the boom arm).

Furthermore, in some implementations, the system may be configured toinclude infra-red (IR) or near infra-red (NIR) illumination/cameras forbetter performance in low-light conditions. In some suchimplementations, retroreflective tape can be positioned on the linkagecomponents to better distinguish particular components from the rest ofthe image frame. For example, the system might be configured to capturea pair of images—one with active illumination in IR or NIR and onewithout—and to compare the captured image data to isolate machinecomponents from the image background.

Although many of the examples above discuss using either image-basedmechanisms or sensor-based mechanisms separately, in someimplementations, data from sensors and/or user input controls can beused to supplement the image data for training and/or use of the neuralnetwork. For example, based on a previously determined pose of the boomarm and a user control input, the number of possible positions for theboom arm can be greatly reduced. In other words, the distance (e.g.,angle) that are particular component of the boom arm has moved since aprior position determination is limited, for example, by the highestpossible speed of the actuator used to adjust the position of thecomponent. Accordingly, a new pose of the boom arm can only differ fromthe previously determined pose of the boom arm by a known (ordeterminable) range. Similarly, in some implementation, other imageprocessing techniques—including, for example, blob tracking,marker-based tracking, and template matching—can be applied to the imagedata and provided as inputs to the neural network or used in parallelwith the neural network to provide redundant determinations forcross-checking.

In the examples above, the neural network is trained by capturing imagedata from an actual machine and comparing the captured image data withthe known positions of the boom arm as determined by other sensors.However, in some implementations, training of the neural network can beperformed or supplemented using virtual images of a 3D model of themachine. For example, a 3D digital model of the machine can be generatedin a computing environment and a software process can be configured togenerate virtual images of the machine with the boom arm in variousdifferent poses. Because the pose of the 3D model of the machine iscontrolled by the software process, the pose of the boom armcorresponding to each virtual image is known. Accordingly, the softwareprocess can be configured to train a neural network based on the virtualimages. In some implementations, a neural network trained using a 3Dmodel of the machine and virtual images can be tested and refined usingan actual machine equipped with one or more cameras and boom armsensors. However, using the virtual images to initially train the neuralnetwork can greatly reduce the time required to train a robust neuralnetwork. Furthermore, in some implementations, the system can beconfigured to generate a “processed image library” in a digital formatwhich allows for a fast numerical image comparison and datainterpolation.

Finally, in some implementations, the methods described above might beadapted to operate with other types of sensors, other types ofactuators, and/or other types of imaging modalities. Although theexamples described above focus on the use of hydraulic cylinders as theactuators for controllably adjusting the pose of the boom arm, in someimplementations, the system might be configured to utilize otheractuators including, for example, linear motors or pneumatic devices.Similarly, although the examples describes above focus on displacementsensors that are configured to measure the displacement of a piston in ahydraulic cylinder in order to measure the pose of the boom arm, in someimplementations, other types of sensors might be utilized including, forexample, rotational sensors, radar/lidar, or sonar to directly measure aposition of one or more of the boom components. Additionally, althoughthe examples described above generally discuss capturing and processing“images” in order to train and utilize the neural network, differentspecific imaging modalities might be utilized in various differentimplementations. For example, some implementation may be configured tocapture and utilize color images while other implementationscapture/utilize black-and-white image. Other implementations mightutilize video image data while still other implementations may utilizesurface scanning and/or structure light projection techniques as inputsto the neural network.

Thus, the invention provides, among other things, systems and methodsfor using and training a neural network to determine a pose of a boomarm of a machine based on captured image data. Various features andadvantages of the invention are set forth in the following claims.

What is claimed is:
 1. A system comprising: a machine including amachine body, an articulating boom arm including a hoist boom coupled tothe machine body and a stick boom coupled to a distal end of the hoistboom, a hoist actuator configured to controllably adjust an angle of thehoist boom relative to the machine body, and a stick actuator configuredto controllably adjust an angle of the stick boom relative to the hoistboom; a hoist sensor configured to output a signal indicative of theangle of the hoist boom relative to the machine body that is directlymeasured by the hoist sensor; a stick sensor configured to output asignal indicative of the angle of the stick boom relative to the hoistboom that is directly measured by the stick sensor; a camera mounted tothe machine with a field of view that includes at least a part of themachine; an electronic processor configured to train a neural network todetermine, based on image data captured by the camera, at least oneselected from a group consisting of the angle of the hoist boom relativeto the machine body and the angle of the stick boom relative to thehoist boom, wherein the signal output by the hoist sensor, the signaloutput by the stick sensor, and an image captured by the camera are usedas training input to train the neural network; determine an actual poseof the articulating boom arm based on at least one selected from a groupconsisting of the signal output from the hoist sensor, the signal outputby the stick sensor, and the output of the neural network based on imagedata captured by the camera; and operate the hoist actuator and thestick actuator based at least in part on the determined actual pose ofthe articulating boom arm.
 2. The machine of claim 1, wherein theelectronic processor is configured to train the neural network bytraining the neural network only while the hoist sensor and the sticksensor are operating properly, wherein the electronic processor isfurther configured to determine whether the hoist sensor is operatingproperly; and determine whether the stick sensor is operating properly,and wherein the electronic processor is configured to determine theactual pose of the articulating boom arm by determining the actual poseof the articulating boom arm based on the signal output from the hoistsensor and the signal output from the boom sensor in response todetermining that both the hoist sensor and the stick sensor areoperating properly, and determining the actual pose of the articulatingboom arm based on the output of the neural network based on image datacaptured by the camera in response to determining that the hoist sensorand the stick sensor are not both operating properly.
 3. The machine ofclaim 1, wherein the hoist actuator includes a hydraulic hoist cylinderconfigured to increase the angle of the hoist boom relative to themachine body by extending a piston of the hydraulic hoist cylinder andto decrease the angle of the hoist boom relative to the machine body byretracting the piston of the hydraulic hoist cylinder, and wherein thehoist sensor includes a displacement sensor configured to directlymeasure a linear displacement of the piston and to output a signalindicative of the linear displacement of the piston.
 4. The machine ofclaim 3, wherein the electronic processor is further configured todetermine whether the hoist sensor is operating properly, and whereinthe electronic processor is configured to determine the actual pose ofthe articulating boom arm by determining an actual angle of the hoistboom relative to the machine body based on the output of thedisplacement sensor in response to determining that the hoist sensor isoperating properly, and determining the actual angle of the hoist boomrelative to the machine body based on the output of the neural networkbased on image data captured by the camera in response to determiningthat the hoist sensor is not operating properly.
 5. The machine of claim1, wherein the stick actuator includes a hydraulic stick cylinderconfigured to increase the angle of the stick boom relative to the hoistboom by extending a piston of the hydraulic stick cylinder and todecrease the angle of the stick boom relative to the hoist boom byretracting the piston of the hydraulic stick cylinder, and wherein thestick sensor includes a displacement sensor configured to directlymeasure a linear displacement of the piston and to output a signalindicative of the linear displacement of the piston.
 6. The machine ofclaim 5, wherein the electronic processor is further configured todetermine whether the stick sensor is operating properly, and whereinthe electronic processor is configured to determine the actual pose ofthe articulating boom arm by determining an actual angle of the stickboom relative to the hoist boom based on the output of the displacementsensor in response to determining that the stick sensor is operatingproperly, and determining the actual angle of the stick boom relative tothe hoist boom based on the output of the neural network based on imagedata captured by the camera in response to determining that the sticksensor is not operating properly.
 7. The machine of claim 1, wherein thecamera is mounted on the machine body and positioned with a field ofview including at least part of the articulating boom arm.
 8. Themachine of claim 1, wherein the camera is mounted on an end effector ofthe articulating arm and positioned with a field of view including atleast part of the articulating boom arm.
 9. The machine of claim 1,wherein the camera is mounted on an end effector of the articulating armand positioned with a field of view including at least part of themachine body.
 10. The machine of claim 1, wherein at least one selectedfrom a group consisting of the hoist sensor and the stick sensorincludes an inertial measurement unit.
 11. The machine of claim 1,further comprising a plurality of cameras, wherein each camera of theplurality of cameras is mounted on the machine and positioned to capturea different portion of the machine or a different perspective of atleast a portion of the machine, and wherein the electronic processor isconfigured to train the neural network by training the neural networkbased on image data from each of the plurality of cameras.
 12. Themachine of claim 1, wherein at least a portion of the machine includes areflective paint, and wherein the electronic processor is furtherconfigured to filter the image data from the camera to distinguish thereflective paint on the machine from an image background beforeproviding the captured image data as input to the neural network. 13.The machine of claim 1, wherein the electronic processor is configuredto train the neural network by training the neural network based on theimage data captured by the camera, a previously determined pose of thearticulating boom arm, and known limits of at last one actuator selectedfrom a group consisting of the hoist actuator and the stick actuator.14. A method for controlling movement of an articulating boom arm of amachine, the method comprising: capturing at least one image with acamera mounted on the machine, wherein the field of view of the cameraincludes at least a portion of the machine; applying an artificialintelligence mechanism with the at least one image as an input to theartificial intelligence mechanism, wherein the artificial intelligencemechanism is trained to output at least one value indicative of a poseof the articulating boom arm based on the at least one image as theinput; and operating an actuator configured to cause movement of thearticulating boom arm based at least in part on the output of theartificial intelligence mechanism.
 15. A method of training a neuralnetwork for determining a pose of an articulating boom arm of a machinebased on captured image data, the method comprising: receiving, by anelectronic processor from a sensor, a signal indicative of a measuredpose of at least one part of the articulating boom arm, wherein thesensor is configured to directly measure a pose of the at least one partof the articulating boom arm; receiving, by the electronic processorfrom a camera mounted to the machine, an image of at least part of themachine; and training the neural network to determine the pose of the atleast one part of the articulating boom arm based on one or morecaptured images, wherein the signal indicative of the measured pose fromthe sensor and the image captured by the camera are used as traininginput to train the neural network.